Seguimiento de costos EC2 y S3 para ML Plantilla

This template provides a comprehensive framework for managing and forecasting AWS cloud expenditures specifically tailored for machine learning workflows. It features dedicated sections for EC2 instance tracking, EBS and S3 storage costs, and granular logs for model training and inference sessions. Users can input specific instance rates, such as the g6.xlarge, and account for hidden costs like public IPv4 addresses and data transfer fees. The built-in calculators automatically translate training durations and image processing counts into precise monetary values, allowing for a clear comparison between different model configurations and dataset sizes.

Managing AI infrastructure often leads to unpredictable billing due to the high cost of GPU instances and varying storage needs. This tool solves that problem by centralizing all cost variables into a single dashboard. It is particularly useful for ML engineers and project managers who need to justify infrastructure spending or optimize training parameters like epochs and image sizes against a fixed budget. By logging every run—including start times, end times, and specific hyperparameters—you gain full visibility into the ROI of your compute resources. The template helps you track exactly how long training took on specific configurations, ensuring that you can identify the most cost-effective setup for your specific SKU and dataset requirements.

To use this template, start by navigating to the Configuration sheet to enter your current AWS region rates for EC2 instances, S3 storage tiers, and IP addresses. Next, use the Training Log to record your model runs, entering the dataset name, epoch count, and timestamps; the template will automatically calculate the duration and associated cost based on your instance type. Finally, use the Inference Tracker to input the number of images processed or the total active time to see the per-image and total session cost. This systematic approach ensures you always have an audit-ready summary of your cloud investment.

Expected benefits: Gain precise insights into the cost-per-model for your AI development lifecycle and significantly reduce the risk of cloud overspending through proactive tracking.